Determinants of greenhouse gas emissions by Brazilian agricultural sector

RODRIGUES, MARCOS; RODRIGUES, MARÍLIA DANYELLE N.; BORGES, RAYSA P.; AZEVEDO JUNIOR, WLADIMIR C. DE; EBLING, ÂNGELO AUGUSTO

doi:10.1590/0001-3765202420240345

Brasil

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ECOSYSTEMS • An. Acad. Bras. Ciênc. 96 (suppl 1) • 2024 • https://doi.org/10.1590/0001-3765202420240345 linkcopy

Determinants of greenhouse gas emissions by Brazilian agricultural sector

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

The agricultural sector is one of the most polluting economic activities, contributing significantly to greenhouse gas (GHGs) emissions. Brazil is one of the largest agricultural producers worldwide and plays a major role in reducing the environmental impact of this sector. Here, we aimed to determine the impact of the agricultural sector, with special attention to production, prices, and trade openness, on the short- and long-term GHGs emissions of Brazilian agriculture. Employing data from 1974 to 2019, we tested the cointegration of variables and compared the determinants of GHG emissions using Autoregressive Distributed Lag (ARDL) and Vector Error Correction Model (VECM) methods. Our results show a long-term equilibrium trend for Brazilian agricultural GHGs emissions, a result that correlates with emerging environmental compliance, and society demands the adoption of sustainable technologies, processes, and policies. In the short run, both cattle herds and Agricultural Added Value to GDP per capita showed an expected positive and significant contribution to GHGs emissions, while agricultural crop area demonstrated an inverse relationship. The trade openness index confirmed that foreign trade plays an important role in reducing GHGs emissions. The price index is not significant in our models. Both the private and public sectors have important roles in sustainable agriculture, especially in increasing system efficiency through the adoption of management and technologies that reduce GHG emissions levels.

Key words ARDL; Brazil; cointegration; grains; pasture; VECM

Variable	Description	Mean	Min	Max	SD
GHGs	Kilotonnes of CO² equivalent	396347.26	221223.19	528425.86	96289.88
APA	Agricultural production area, in hectares	56482194.91	41686048	80610018	10749633.50
CH	Cattle Herd, in heads.	164883480.72	92495364	218190768	39595401.40
TO	Trade openness index	0.214	0.144	0.297	0.047
AGDPpc	Agriculture, Forestry, and Fishing Value Added to GDP per capita –in dollars–	6997.32	5032.18	9247.57	1176.23
API	Agricultural price index –in Brazilian currency per ton–	1478.78	427.96	3212.43	719.26

Model	Variable	ADF test		P-P test
Model	Variable	Level - I(0)	1 ^st diff - I(1)	Level - I(0)	1 ^st diff - I(1)
Intercept and trend	GHGs	-2.423	-5.640 *	-2.099	-6.078 *
	APA	-1.231	-5.133 *	-1.598	-7.866 *
	CH	-1.030	-4.651 *	-1.960	-5.723 *
	TO	-3.214	-5.539 *	-3.079	-6.747 *
	AGDPpc	-2.363	-5.911 *	-2.520	-7.696 *
Intercept	GHGs	-2.580	-5.186 *	-2.842	-5.500 *
	APA	0.015	-5.083 *	-0.236	-7.819 *
	CH	-1.534	-3.748 *	-2.911	-5.428 *
	TO	-1.735	-5.606 *	-1.576	-6.860 *
	AGDPpc	-1.484	-5.969 *	-1.497	-7.823 *
None	GHGs	3.077	-3.748 *	4.377	-4.008 *
	APA	2.271	-4.443 *	2.342	-7.156 *
	CH	2.732	-2.323 *	4.418	-4.405 *
	TO	-0.775	-5.594 *	-0.619	-6.644 *
	AGDPpc	-0.532	-5.951 *	-0.744	-7.656 *

ARDL Bounds test		Model Diagnostic
Test	value	Test	value	p-value
I(0) - lower bound	3.178	Breusch-Godfrey LM Test	χ² = 0.236	0.791
I(1) - upper bound	4.450	Jarque–Bera test Test for Normality	χ² = 2.597	0.273
F-statistic	34.897	Breusch-Pagan-Godfrey test of heteroskedasticity	χ² = 0.557	0.822
		Ramsey RESET Test	F = 0.019	0.891

Long run estimate		Short-run estimate
Regressor	Coefficient (sth. Error)	Regressor	Coefficient (sth. Error)
AVP	0.057 (0.035)	∆GHGs_t-1	0.154 (0.069) *
CH	0.989 (0.044) *	∆APA_t	0.048 (0.030)
TO	-0.041 (0.019)*	∆CH_t	0.837 (0.079) *
AGDPpc	0.005 (0.024)	∆TO_t	0.016 (0.019)
Constant	-6.922 (0.578) *	∆TO_t-1	-0.051 (0.018) *
		∆AGDPpc_t	0.002 (0.018)
		∆AGDPpc_t-1	0.0367 (0.019)
		∆AGDPpc_t-2	-0.034 (0.016) *
		API_t-1	-0.002 (0.002)
		C	-5.856 (0.722) *
		ECT	-0.846 (0.060) *

Cointegration vectors	Trace statistic	Maximum Eigenvalue
None	77.892 *	34.308 *
At most 1	43.583	19.539
At most 2	24.045	16.060
At most 3	6.985	6.666
At most 4	0.319	0.319

Long run estimate		Short-run estimate
Regressor	Coeficient (Sth. Error)	Regressor	Coeficient (Sth. Error)
GHGs_t-1	1	∆GHGs_t-1	-0.077 (0.192)
AVP_t-1	-0.091 (-0.035) *	∆APA_t-1	-0.168 (0.074) *
CH_t-1	-0.913 (-0.038) *	∆CH_t-1	0.540 (0.236) *
TO_t-1	-0.017 (-0.017)	∆TO_t-1	-0.008 (0.029)
AGDPpc_t-1	0.028 (-0.021)	∆AGDPpc_t-1	0.074 (0.026) *
C	5.816	API _t-1	-0.003 (0.003)
		C	0.036 (0.020)
		ECT	-0.481 (-0.237) *

Null hypothesis	Test statistics	p-value
lnAVP lnGHGs	5.032	0.025 *
lnCH lnGHGs	5.556	0.018 *
lnTO lnGHGs	0.235	0.628
lnAGDPpc lnGHGs	7.520	0.006 *
lnGHGs lnAVP	1.368	0.242
lnGHGs lnCH	0.571	0.450
lnGHGs lnTO	0.464	0.496
lnGHGs lnAGDPpc	4.309	0.038 *

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[tfnII] Note: GHGs: Greenhouse gases emissions, in kilotonnes of CO² equivalent; APA: agricultural production area, in hectares; CH: cattle herd; TO: trade openness index; AGDPpc: Agriculture, Forestry, and Fishing Value Added to GDP per capita. * Significant at 5%.